INJEX system

[Development, morphogenesis, and regeneration of cardiac Purkinje fibers].

Boulgakoff Lucie L, Miquerol Lucile L

Ventricular arrhythmias often originate in the Purkinje system, a network of specialized cardiomyocytes located on the luminal surface of the ventricles. The Purkinje system is part of the ventricular conduction system, which ensures the rapid propagation of electrical activity through the ventricles in order to synchronize their contractions. Although these cells represent only a tiny fraction of the ventricular mass, their pathogenic role is disproportionate. During embryogenesis, the ventricular conduction system arises from different populations of cardiac progenitors. Differentiation into fast-conducting cardiomyocytes occurs gradually as the ventricles develop, resulting in the formation of a single electrical circuit by the end of the fetal stage. This review highlights the structure-function relationship between the morphogenesis of the ventricular conduction system and conduction defects, and discusses recent data on the origin, development, and regeneration of the Purkinje fiber network.

Cascaded adaptive load frequency control for single area and double area power systems considering wind penetration.

El-Bassiouny Ahmed A, Attia Mahmoud A MA, Hamouda Rizk R, Hamouda Mohamed R MR

This study investigates the application of a cascaded adaptive controller for Load Frequency Control (LFC) in single-area and two-area power systems. The controller is a combination between adaptive PI controller and PID controller thus the term cascaded controller. The main objective is to evaluate the controller's performance under various operating conditions through comparison with a conventional controller and adaptive controller. The case studies in the single-area power system shall be four case studies to be examined: (1) a conventional system without renewable energy integration, (2) a system with wind power introduced as a disturbance source, and (3) and (4) modified versions of the first two cases excluding time-delay effects. Similarly, the two-area power system is analyzed using four case studies, for the first and second scenarios, a static load change is applied independently to each area, while the third and fourth scenarios extend these cases by considering dynamic, time-varying load changes. For all scenarios, disturbances are introduced in one area, and their effects on tie-line power flow are analyzed. An optimization algorithm is utilized to determine the optimal gain parameters for each controller configuration. MATLAB/Simulink is employed for system simulation, and the system responses are assessed in the time domain. Simulation results demonstrate that the proposed cascaded adaptive controller exhibits superior performance and robustness, particularly in disturbance rejection and frequency stability enhancement. As there is an improvement in the system response by minimizing overshoot, reducing oscillations, and achieving faster settling times-outperforming traditional PI, standalone AFOPI, and PID controllers indicating that it can be utilized in different power systems.

Deep learning-based automated assessment of alveolar bone loss in CBCT for periodontitis.

Wang Yuyan Y, Zheng Hongjie H, Duan Xinyu X, Li Yihong Y et al.

This study aimed to develop and validate a CBCT-based automated system for assessing radiographic alveolar bone loss (RBL) to improve the accuracy and efficiency of periodontitis diagnosis. A total of 110 patients (2,796 teeth) with Stage I-IV periodontitis from four center were included. The nnU-Net framework was used to segment teeth, alveolar bone, and the cemento-enamel junction (CEJ). RBL was calculated automatically using an edge-constrained shortest path algorithm. The model was trained on data from Center A and externally validated with datasets from Centers B-D. Linear periodontal measurements from 11 CBCT scans were compared between manual and CAD-based segmentation. An independent validation set was used to assess automated staging accuracy and time efficiency. The CAD system achieved Dice similarity coefficients (DSC) of 95.85% for teeth, 95.75% for alveolar bone, and 86.18% for the CEJ. External validation showed alveolar bone and tooth DSC values both above 95% and CEJ DSC values above 77%. Linear measurements showed strong agreement with manual segmentation (Spearman's ρ = 0.9187; ICC = 0.9266). For staging, the CAD system reached an overall accuracy of 87.31%, with 97.01% for Stage Ⅰ, 88.06% for Stage Ⅱ and 89.55% for Stage Ⅲ/Ⅳ. The CAD system represented a 13.42-fold acceleration compared with the manual workflow. The CAD system enables accurate automated segmentation and RBL quantification on CBCT images, with robust multicenter performance and substantial gains in efficiency. This system offers a fast and reliable method for RBL assessment, supporting consistent diagnosis and monitoring of periodontitis.

Editorial: Understanding neural processing as an integrated intelligent system.

Werbos Paul J PJ, Wack David S DS, Slavakis Konstantinos K, Badgaiyan Rajendra D RD